Inferring Neural Connectivity and the Underlying Network Dynamics from Spike Train Recordings
نویسندگان
چکیده
A novel method for the identification and modeling of neural networks using experimental spike trains is discussed. The method assumes a reference model of interconnected deterministic integrate-and-fire neurons and fit the parameters of the model to the observed experimental spike trains. The identification provides the properties of the individual synapses and neurons, hence extracting the functional connectivity between neurons. The method is shown to be effective when applied on simulated data.
منابع مشابه
A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings.
In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network obtained by our method includes explicit mathematical models of each of the spiking neurons and a description of the effective connectivity ...
متن کاملBayesian latent structure discovery from multi-neuron recordings
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. In particular, they do not take advantage of the rich temporal dependencies in multi-neuron recording...
متن کاملSuccessful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone
Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from...
متن کاملA Statistical Approach to Functional Connectivity Involving Multichannel Neural Spike Trains
RUIWEN ZHANG : A Statistical Approach to Functional Connectivity Involving Multichannel Neural Spike Trains. (Under the direction of Young K. Truong and Haipeng Shen.) The advent of the multi-electrode has made it feasible to record spike trains simultaneously from several neurons. However, the statistical techniques for analyzing large-scale simultaneously recorded spike train data have not de...
متن کاملReconstruction of sparse connectivity in neural networks from spike train covariances
The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of rec...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004